Developing the QSPR model for predicting the storage lipid/water distribution coefficient of organic compounds

Miao Li, Jian Li, Yuchen Lu, Cenyang Han, Xiaoxuan Wei, Guangcai Ma, Haiying Yu

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Front. Environ. Sci. Eng. ›› 2021, Vol. 15 ›› Issue (2) : 24. DOI: 10.1007/s11783-020-1316-z
RESEARCH ARTICLE
RESEARCH ARTICLE

Developing the QSPR model for predicting the storage lipid/water distribution coefficient of organic compounds

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Highlights

• A predictive model for storage lipid/water distribution coefficient was developed.

• The model yields outstanding fitting performance, robustness, and predictive ability.

• Hydrophobic and electrostatic interactions and molecular size dominate log Klip/w.

• The model can be used in a wide application domain to predict log Klip/w values.

Abstract

The distribution of organic compounds in stored lipids affects their migration, transformation, bioaccumulation, and toxicity in organisms. The storage lipid/water distribution coefficient (log Klip/w) of organic chemicals, which quantitatively determines such distribution, has become a key parameter to assist their ecological security and health risk. Due to the impossibility to measure Klip/w values for a huge amount of chemicals, it is necessary to develop predictive approaches. In this work, a quantitative structure-property relationship (QSPR) model for estimating log Klip/w values of small organic compounds was constructed based on 305 experimental log Klip/w values. Quantum chemical descriptors and n-octanol/water partitioning coefficient were employed to characterize the intermolecular interactions that dominate log Klip/w values. The hydrophobic and electrostatic interactions and molecular size have been found to play important roles in governing the distribution of chemicals between lipids and aqueous phases. The regression (R2 = 0.959) and validation (Q2 = 0.960) results indicate good fitting performance and robustness of the developed model. A comparison with the predictive performance of other commercial software further proves the higher accuracy and stronger predictive ability of the developed Klip/w predictive model. Thus, it can be used to predict the Klip/w values of cycloalkanes, long-chain alkanes, halides (with fluorine, chlorine, and bromine as substituents), esters (without phosphate groups), alcohols (without methoxy groups), and aromatic compounds.

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Keywords

Storage lipid/water distribution coefficient / log Klip/w / Organic compounds / QSPR / Quantum chemical descriptors

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Miao Li, Jian Li, Yuchen Lu, Cenyang Han, Xiaoxuan Wei, Guangcai Ma, Haiying Yu. Developing the QSPR model for predicting the storage lipid/water distribution coefficient of organic compounds. Front. Environ. Sci. Eng., 2021, 15(2): 24 https://doi.org/10.1007/s11783-020-1316-z

References

[1]
Alava J J, Keller J M, Kucklick J R, Wyneken J, Crowder L, Scott G I (2006). Loggerhead sea turtle (Caretta caretta) egg yolk concentrations of persistent organic pollutants and lipid increase during the last stage of embryonic development. Science of the Total Environment, 367(1): 170–181
CrossRef Google scholar
[2]
Avdeef A (2012). Absorption and Drug Development: Solubility, Permeability, and Charge State. Hoboken: John Wiley & Sons
[3]
Bakire S, Yang X Y, Ma G C, Wei X X, Yu H Y, Chen J R, Lin H J (2018). Developing predictive models for toxicity of organic chemicals to green algae based on mode of action. Chemosphere, 190: 463–470
CrossRef Google scholar
[4]
Bittermann K, Spycher S, Endo S, Pohler L, Huniar U, Goss K U, Klamt A (2014). Prediction of phospholipid–water partition coefficients of ionic organic chemicals using the mechanistic model COSMO mic. Journal of Physical Chemistry B, 118(51): 14833–14842
CrossRef Google scholar
[5]
Carlsson K, Karlberg B (2000). Determination of octanol–water partition coefficients using a micro-volume liquid-liquid flow extraction system. Analytica Chimica Acta, 423(1): 137–144
CrossRef Google scholar
[6]
Chen J W, Li X H, Yu H Y, Wang Y N, Qiao X L (2008). Progress and perspectives of quantitative structure-activity relationships used for ecological risk assessment of toxic organic compounds. Science in China. Series B, Chemistry, 51(7): 593–606
CrossRef Google scholar
[7]
Chen J W, Xue X Y, Schramm K W, Quan X, Yang F L, Kettrup A (2002). Quantitative structure–property relationships for octanol–air partition coefficients of polychlorinated biphenyls. Chemosphere, 48(5): 535–544
CrossRef Google scholar
[8]
Cherkasov A, Muratov E N, Fourches D, Varnek A, Baskin I I, Cronin M, Dearden J, Gramatica P, Martin Y C, Todeschini R, Consonni V, Kuzmin V E, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Gasteiger J, Richard A, Tropsha A (2014). QSAR modeling: where have you been? Where are you going to? Journal of Medicinal Chemistry, 57(12): 4977–5010
CrossRef Google scholar
[9]
Endo S, Brown T N, Goss K U (2013). General model for estimating partition coefficients to organisms and their tissues using the biological compositions and polyparameter linear free energy relationships. Environmental Science & Technology, 47(12): 6630–6639
CrossRef Google scholar
[10]
Endo S, Escher B I, Goss K U (2011). Capacities of membrane lipids to accumulate neutral organic chemicals. Environmental Science & Technology, 45(14): 5912–5921
CrossRef Google scholar
[11]
Feng Q Y, Wu T, Wan Y, Liu X Q, Liu Y (2017). Environmental behavior of persistent organic pollutants in aquatic ecosystem. Acta Ecologica Sinica, 37(9): 2845–2857
[12]
Frisch M J, Trucks G W, Schlegel H B, Scuseria G E, Robb M A, Cheeseman J R, Scalmani G, Barone V, Mennucci B, Petersson G A, Nakatsuji H, Caricato M, Li X, Hratchian H P, Izmaylov A F, Bloino J, Zheng G, Sonnenberg J L, Hada E M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Vreven T, Montgomery J A, Peralta J, Ogliaro J E, Bearpark F, Heyd M, Brothers J J, Kudin E, Staroverov K N, Kobayashi V N, Normand R, Raghavachari J, Rendell K, Burant A, Iyengar J C, Cossi T S S, Rega N, Millam J M, Klene M, Knox J E, Cross J B, Bakken V, Adamo C, Jaramillo J, Gomperts R, Stratmann R E, Yazyev O, Austin A J, Cammi R, Pomelli C, Ochterski J W, Martin R L, Morokuma K, Zakrzewski V G, Voth G A, Salvador P, Dannenberg J J, Dapprich S, Daniels F A D, Foresman J B, Ortiz J V, Cioslowski J, Fox D J (2013). Gaussian 09, Revision D.01. Gaussian, Inc.: Wallingford CT
[13]
Geisler A, Endo S, Goss K U (2012). Partitioning of organic chemicals to storage lipids: elucidating the dependence on fatty acid composition and temperature. Environmental Science & Technology, 46(17): 9519–9524
CrossRef Google scholar
[14]
Geisler A, Oemisch L, Endo S, Goss K U (2015). Predicting storage–lipid water partitioning of organic solutes from molecular structure. Environmental Science & Technology, 49(9): 5538–5545
CrossRef Google scholar
[15]
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten I H (2009). The WEKA data mining software: An update. ACM SIGKDD Explorations Newsletter, 11(1): 10–18
CrossRef Google scholar
[16]
Hu X Y, Yang T, Liu C, Jin J, Gao B L, Wang X J, Qi M, Wei B K, Zhan Y Y, Chen T, Wang H T, Liu Y T, Bai D R, Rao Z, Zhan N (2020). Distribution of aromatic amines, phenols, chlorobenzenes, and naphthalenes in the surface sediment of the Dianchi Lake, China. Frontiers of Environmental Science & Engineering, 14 (4): 66
[17]
Jiang X M, Wei W L, Xia Z N, Chen Z T (2006). A novel microemulsion electrokinetic chromatography for measuring lipid-water partition coefficients of pharmaceuticals. Acta Pharmaceutica Sinica, 41(10): 1020–1024 (in Chinese)
[18]
Krämer S D (2007). Liposome/water partitioning: Theory, techniques and applications. Pharmacokinetic Optimization in Drug Research: Biological, Physicochemical and Computational Strategies, 401–428
[19]
Liu S, Jin L, Yu H, Lv L, Chen C E, Ying G (2020). Understanding and predicting the diffusivity of organic chemicals for diffusive gradients in thin-films using a QSPR model. Science of the Total Environment, 706: 135691
CrossRef Google scholar
[20]
Ma G C, Yu H Y, Xu X Q, Geng L M, Wei X X, Wen J L, Wang ZG (2020). Molecular basis for metabolic regioselectivity and mechanism of cytochrome P450s toward carcinogenic 4-(methylnitrosamino)-(3-pyridyl)-1-butanone. Chemical Research in Toxicology, 33(2): 436–447
CrossRef Google scholar
[21]
Ma G C, Yuan Q, Yu H Y, Lin H J, Chen J R, Hong H C (2017). Development and evaluation of predictive model for bovine serum albumin-water partition coefficients of neutral organic chemicals. Ecotoxicology and Environmental Safety, 138: 92–97
CrossRef Google scholar
[22]
McLachlan M S (1995). Bioaccumulation of hydrophobic chemicals in agricultural food chains. Environmental Science & Technology, 30(1): 252–259
CrossRef Google scholar
[23]
Poole C F, Atapattu S N, Poole S K, Bell A K (2009). Determination of solute descriptors by chromatographic methods. Analytica Chimica Acta, 652(1–2): 32–53
CrossRef Google scholar
[24]
Sikkema J, De Bont J A, Poolman B (1995). Mechanisms of membrane toxicity of hydrocarbons. Microbiological Reviews, 59(2): 201–222
CrossRef Google scholar
[25]
Tian L (2010). “GsGrid: Extracting data from Gaussian grid file and grid file calculation”, Version 1.7, Available at the website of gsgrid.codeplex.com
[26]
United States Environmental Protection Agency (U.S. EPA) (2015). Estimation Programs Interface Suite™ for Microsoft® Windows, V. 4.11, Microsoft Inc.: Washington, DC, USA, Available at the website of www.epa.gov/oppt/exposure/pubs/episuite.htm.
[27]
Wang B, Chen J W, Li X H, Wang Y N, Chen L, Zhu M, Yu H Y, Kühne R, Schüürmann G (2009). Estimation of soil organic carbon normalized sorption coefficient (Koc) using least squares-support vector machine. QSAR & Combinatorial Science, 28(5): 561–567
CrossRef Google scholar
[28]
Wang Z, Zeng X, Zhai Z (2008). Prediction of supercooled liquid vapor pressures and n-octanol/air partition coefficients for polybrominated diphenyl ethers by means of molecular descriptors from DFT method. Science of the Total Environment, 389(2–3): 296–305
CrossRef Google scholar
[29]
Wei X X, Li M, Wang Y F, Jin L M, Ma G C, Yu H Y (2019). Developing predictive models for carrying ability of micro-plastics towards organic pollutants. Molecules, 24(9): 1784
CrossRef Google scholar
[30]
van Wezel A P, Opperhuizen A (1995). Thermodynamics of partitioning of a series of chlorobenzenes to fish storage lipids, in comparison to partitioning to phospholipids. Chemosphere, 31(7): 3605–3615
CrossRef Google scholar
[31]
Yi X W, Gao Z Q, Liu L H, Zhu Q, Hu G J, Zhou X H (2020) Acute toxicity assessment of drinking water source with luminescent bacteria: Impact of environmental conditions and a case study in Luoma Lake, East China. Frontiers of Environmental Science & Engineering, 14 (6): 109
[32]
Yu H, Wondrousch D, Li F, Chen J, Lin H, Ji L (2015). In silico investigation of the thyroid hormone activity of hydroxylated polybrominated diphenyl ethers. Chemical Research in Toxicology, 28(8): 1538–1545
CrossRef Google scholar

Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (Nos. 21707122 and 21677133) and National College Students Innovation and Entrepreneurship Training Program (No. 202010345069), which is gratefully acknowledged.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11783-020-1316-z and is accessible for authorized users.

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